School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China.
Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52246, United States.
Sci Rep. 2019 Jan 14;9(1):103. doi: 10.1038/s41598-018-36454-5.
Sputum deposition blocks the airways of patients and leads to blood oxygen desaturation. Medical staff must periodically check the breathing state of intubated patients. This process increases staff workload. In this paper, we describe a system designed to acquire respiratory sounds from intubated subjects, extract the audio features, and classify these sounds to detect the presence of sputum. Our method uses 13 features extracted from the time-frequency spectrum of the respiratory sounds. To test our system, 220 respiratory sound samples were collected. Half of the samples were collected from patients with sputum present, and the remainder were collected from patients with no sputum present. Testing was performed based on ten-fold cross-validation. In the ten-fold cross-validation experiment, the logistic classifier identified breath sounds with sputum present with a sensitivity of 93.36% and a specificity of 93.36%. The feature extraction and classification methods are useful and reliable for sputum detection. This approach differs from waveform research and can provide a better visualization of sputum conditions. The proposed system can be used in the ICU to inform medical staff when sputum is present in a patient's trachea.
痰液沉积阻塞患者气道，导致血氧饱和度降低。医护人员必须定期检查插管患者的呼吸状态。这一过程增加了医护人员的工作量。在本文中，我们描述了一个从插管患者获取呼吸声的系统，提取音频特征，并对这些声音进行分类，以检测痰液的存在。我们的方法使用了从呼吸声的时频谱中提取的 13 个特征。为了测试我们的系统，收集了 220 个呼吸声样本。其中一半的样本是从有痰的患者身上采集的，其余的是从没有痰的患者身上采集的。测试是基于十折交叉验证进行的。在十折交叉验证实验中，逻辑分类器识别有痰的呼吸声的灵敏度为 93.36%，特异性为 93.36%。特征提取和分类方法对于痰液检测是有用和可靠的。这种方法与波形研究不同，可以更好地可视化痰液情况。所提出的系统可以在 ICU 中使用，以便在患者气管中有痰液时通知医护人员。
